1. Causal-Comparative Studies Presented by: Taryl Hargens Lynda Johnson Veronica Vasquez Kyann McMillie January 29, 2009

2. Logic of Causal-Comparative Designs Starts with effects and works backward in time to identify causes Independent variable is “ ex post facto ” (after the fact) Independent variable cannot be manipulated Self-selected groups since research cannot control characteristics As each alternative hypothesis is ruled out, causal claim gains credibility, but alternative is not considered a "false finding."

3. Difference between true experiment and causal-comparative Correlation-causation fallacy- a mistake made when a statistical relationship between two variables infers that one causes the other Spuriousness- a third-variable may may be responsible Direction of causality problem- incorrectly identifying WHICH is the cause and which is the effect. Post hoc fallacy- an effect is observed and the event that occurred before is said to have caused the effect..."after this, therefore BECAUSE of this."

4. Causal-Comparative Designs Three designs for controlling extraneous variables and fallacies: Between-Groups Design Path Models Archival Time Series

5. Between-Group Design Scores of the sample on the DV are subdivided into two or more groups which represent the levels or values of the IV. Classification of the IV may have: Subject characteristics (sex, birth order) or Be determined by status (graduated from college, attendance at a private school, had a mother with schizophrenia)

6. Between-Group Design For example: What is the effect of Birth Order on Achievement Motivation? Statistical tests for academic achievement are calculated for: First borns Second borns Later borns When the means are compared, a statistically significant difference between the subgroups is evidence that the IV and DV are related.

7. Statistical Correlation It might look like this: What is the effect of Birth Order on Achievement Motivation? BIRTH ORDER First-born Second-born Later-born Achievement mean 5.6 5.3 4.9 Motivation SD 1.2 1.2 1.7 F= 0.90; p is greater than 0.05

8. Criterion for determining Cause and Effect First, document a statistical relationship between the DV and the IV. Then, establish that the IV occurred BEFORE the DV (temporal sequence).

9. Temporal Sequence Poses no difficulty with some: What is the effect of birth order on academic motivation? It is logical to assume one must be born before developing the capacity to achieve academic motivation. Does college graduation increase life-long earnings? It is illogical to assert that life-time earnings could lead to college graduation.

10. Temporal Sequence Other sequences are more difficult to establish and require more plausible arguments: Does a schizophrenic mother cause her children's schizophrenia? It is merely conjecture as to whether schizophregenic mothering (IV) is the cause of schizophrenia in the child, or the child's schizophrenia (DV) was present before the mothering of the child.

11. Extraneous Influences Does college graduation increase life-long earnings? Only an incidental correlate, because even when a... 1.Statistical relationship can be determined; and 2.Temporal Sequence can be established. A third-variable might threaten the causal relationship, such as... Could SES status be the cause of both? What about level of intelligence?

12. Methods of Control Cross Classification of IV and DV (holding the third variable constant and comparing the two analyses) For example, with SES held constant, the relationship between each category of SES (high/low) are compared to the relationships between the IV and DV.

13. Methods of Control Post Hoc Matching (matching subjects on plausible third variables then comparing for differences on the DV) Still doesn't remove the non-equivalence threat to internal validity: Extraneous influences Regression in extreme group studies

14. Methods of Control Analysis of the Covariate Statistically removes the extraneous variance between two groups on a third variable. Third variable as a covariate or as a control variable by only selecting subjects that possess the given characteristic and changing the research question... "Among _____ what is the effect of _____ on _____?"

15. Cautions Be aware of non-accounted for third variables. Watch for ambiguous criteria for inclusion. Recognize that statistical differences are only the first step toward causality, not proof of it.

16. PATH MODELS Path Models = Causal Models Purpose: to test hypotheses about causal relationships among variables where data are in form of correlations At least 3 variables involved Starting point is correlation coefficients between all pairs of variables

17. Conditions that must be met Researcher must begin with theory that specifies the causal relationships among the collection of variables in the model Each variable must be measured - yields path coefficients Path coefficients can be correlation coefficients or partial coefficients Researcher checks coefficients with orginial model (alternative models may be developed)

18. Path Models depend on Accurate measurement of variables Assumption that all plausible causes are included in the model

19. Example of Path Model Effects of Exercise on Cholesterol Independent Variables (exogenous): Eating Saturated Fats Aerobic Exercise Dependent (endogenous) variable: Low density Lipoproteins High Density Lipoproteins

20. Eating Saturated Fats Aerobic Exercise High Density Lipoproteins Low- Density Lipoproteins Independent or exogenous variables Dependent or endogenous variables 0.26 -0.73 -0.55 -0.38

21. Archival Time-Series Useful for evaluating impact of social policy decisions and government programs,and one time events (hurricanes, earthquakes) Researcher can't manipulate the independent variable Independent variable occurs for nonresearch reasons Changes in dependent variable that coincide with the change of the independent variable are evidence of its effect

22. Archival Time-Series Reasons to use this model: o Time-series model o Unravel seasonal trends o Remove long-rage secular trends o Separate the effects of other events and policies during same time

23. Archival Time-Series Be careful with: o Obtaining the necessary data o Making sure the data has not be standardized o Data has been regularly recorded by the responsible party o Instrumentation threat to internal validity